Maintainer: aipicasso

Total Score


Last updated 4/29/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

emi is a text-to-image AI model developed by aipicasso. It is based on Stable Diffusion and focuses on generating high-quality anime-style artwork. The model was trained on a dataset of anime images and can generate detailed, expressive characters and scenes. Compared to similar models like PixArt-XL-2-1024-MS and EimisAnimeDiffusion_1.0v, emi excels at producing transparent and full-body anime characters in a distinct visual style.

Model inputs and outputs

emi is a text-to-image model, meaning it takes text prompts as input and generates corresponding images as output. The text prompts can describe a wide range of anime-style scenes and characters, and the model will attempt to faithfully render them.


  • Text prompt: A description of the desired image, such as "anime artwork, anime style, (1girl), (black bob hair:1.5), brown eyes, red maples, sky, ((transparent))"


  • Generated image: An image that matches the provided text prompt, in this case a transparent anime-style character with black hair, brown eyes, and a nature background.


emi can generate highly detailed and expressive anime-style artwork. The model is particularly adept at rendering transparent elements, intricate clothing and accessories, and full-body character poses. It also performs well on generating natural backgrounds and landscapes to complement the anime characters.

What can I use it for?

The emi model is well-suited for creative and artistic applications, such as generating concept art, illustrations, or visual assets for games, animations, or other media. Its unique anime-inspired style makes it a valuable tool for artists, designers, and content creators working in the anime and manga genres. Additionally, the model's ability to generate transparent elements could be useful for tasks like digital compositing or character design.

Things to try

One interesting aspect of emi is its use of Textual Inversion and the DreamShaper XL1.0 model, which can help improve the quality and consistency of the generated images. Users could experiment with different prompts and negative prompts to further refine the output. Additionally, the model's integration with ComfyUIFreeU and its optimized sampling parameters could be worth exploring to achieve the best results.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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